Signals and Noise in Evoked Brain Potentials

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Abstract
Event-related brain potentials measured with scalp electrodes are always corrupted by unrelated electrical discharges occurring in the brain. These unrelated electrical discharges, generally referred to as noise, have temporal and spectral characteristics similar to evoked potential waveforms, and they greatly increase the difficulty of detecting and estimating the parameters of the evoked potential waveforms themselves. This problem has been analyzed by computing the probability distributions for measured amplitudes and latencies of ERP components measured in the presence of the ongoing EEG. The analytical results have been verified over a wide range of signal-to-noise ratios by computer simulation. Comparisons of theoretical results to measured data indicate that the latency variations found experimentally greatly exceed what would be expected if they were due only to additive noise. It may be concluded, therefore, that the single ERP is not a signal whose components are deterministically related to the stimulus, but is made up of components that shift significantly in both amplitude and latency from one stimulus application to the next. Using the expressions developed in the paper, it is possible to separate the contributions to the variance due to interference from the ongoing EEG and that inherent in the ERP.

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